Overview

Dataset statistics

Number of variables21
Number of observations2217
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory363.9 KiB
Average record size in memory168.1 B

Variable types

Numeric17
DateTime1
Categorical3

Alerts

bathrooms is highly overall correlated with bedrooms and 6 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
floors is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
long is highly overall correlated with zipcodeHigh correlation
price is highly overall correlated with grade and 3 other fieldsHigh correlation
sqft_above is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
yr_built is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
waterfront is highly imbalanced (94.8%)Imbalance
view is highly imbalanced (72.9%)Imbalance
id has unique valuesUnique
sqft_basement has 1346 (60.7%) zerosZeros
yr_renovated has 2121 (95.7%) zerosZeros

Reproduction

Analysis started2024-01-19 15:00:49.911113
Analysis finished2024-01-19 15:01:36.678717
Duration46.77 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct2217
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6280922 × 109
Minimum1000102
Maximum9.8393012 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:36.773005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1714042 × 108
Q12.1177001 × 109
median3.9050809 × 109
Q37.4629 × 109
95-th percentile9.3248804 × 109
Maximum9.8393012 × 109
Range9.8383011 × 109
Interquartile range (IQR)5.3452 × 109

Descriptive statistics

Standard deviation2.9104694 × 109
Coefficient of variation (CV)0.62887022
Kurtosis-1.2980905
Mean4.6280922 × 109
Median Absolute Deviation (MAD)2.4721806 × 109
Skewness0.22543155
Sum1.026048 × 1013
Variance8.470832 × 1018
MonotonicityNot monotonic
2024-01-19T20:31:36.992465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3793500160 1
 
< 0.1%
8651430870 1
 
< 0.1%
9277200180 1
 
< 0.1%
1139000072 1
 
< 0.1%
4077800455 1
 
< 0.1%
123039207 1
 
< 0.1%
9269200120 1
 
< 0.1%
3345100286 1
 
< 0.1%
1954700365 1
 
< 0.1%
6979970150 1
 
< 0.1%
Other values (2207) 2207
99.5%
ValueCountFrequency (%)
1000102 1
< 0.1%
3800008 1
< 0.1%
7200080 1
< 0.1%
7200179 1
< 0.1%
11501330 1
< 0.1%
11510310 1
< 0.1%
11900140 1
< 0.1%
13002460 1
< 0.1%
16000397 1
< 0.1%
16000545 1
< 0.1%
ValueCountFrequency (%)
9839301165 1
< 0.1%
9834201470 1
< 0.1%
9834201215 1
< 0.1%
9828702902 1
< 0.1%
9828202325 1
< 0.1%
9828202255 1
< 0.1%
9828201725 1
< 0.1%
9828201361 1
< 0.1%
9828201020 1
< 0.1%
9828200187 1
< 0.1%

date
Date

Distinct300
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-14 00:00:00
2024-01-19T20:31:37.179645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:37.368005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct975
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean538724.24
Minimum83000
Maximum3850000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:37.578410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum83000
5-th percentile209720
Q1320000
median450000
Q3635000
95-th percentile1182000
Maximum3850000
Range3767000
Interquartile range (IQR)315000

Descriptive statistics

Standard deviation358635.06
Coefficient of variation (CV)0.66571176
Kurtosis15.935121
Mean538724.24
Median Absolute Deviation (MAD)149950
Skewness3.1460339
Sum1.1943516 × 109
Variance1.2861911 × 1011
MonotonicityNot monotonic
2024-01-19T20:31:37.751314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500000 20
 
0.9%
400000 19
 
0.9%
600000 18
 
0.8%
550000 18
 
0.8%
425000 18
 
0.8%
350000 18
 
0.8%
435000 17
 
0.8%
450000 17
 
0.8%
325000 17
 
0.8%
280000 16
 
0.7%
Other values (965) 2039
92.0%
ValueCountFrequency (%)
83000 1
< 0.1%
89000 1
< 0.1%
92000 1
< 0.1%
95000 1
< 0.1%
109500 1
< 0.1%
110000 2
0.1%
114975 1
< 0.1%
119500 1
< 0.1%
123000 1
< 0.1%
130000 2
0.1%
ValueCountFrequency (%)
3850000 1
< 0.1%
3420000 1
< 0.1%
3200000 1
< 0.1%
3170000 1
< 0.1%
3120000 1
< 0.1%
3000000 1
< 0.1%
2950000 1
< 0.1%
2890000 1
< 0.1%
2720000 1
< 0.1%
2700000 1
< 0.1%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.353631
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:37.907510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86726292
Coefficient of variation (CV)0.25860416
Kurtosis1.2340465
Mean3.353631
Median Absolute Deviation (MAD)1
Skewness0.40140298
Sum7435
Variance0.75214498
MonotonicityNot monotonic
2024-01-19T20:31:38.041626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1033
46.6%
4 711
32.1%
2 274
 
12.4%
5 156
 
7.0%
1 20
 
0.9%
6 19
 
0.9%
7 3
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 20
 
0.9%
2 274
 
12.4%
3 1033
46.6%
4 711
32.1%
5 156
 
7.0%
6 19
 
0.9%
7 3
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7 3
 
0.1%
6 19
 
0.9%
5 156
 
7.0%
4 711
32.1%
3 1033
46.6%
2 274
 
12.4%
1 20
 
0.9%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0990077
Minimum0.5
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:38.206786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.5
median2.25
Q32.5
95-th percentile3.5
Maximum6
Range5.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75756349
Coefficient of variation (CV)0.36091507
Kurtosis0.55405408
Mean2.0990077
Median Absolute Deviation (MAD)0.5
Skewness0.44958572
Sum4653.5
Variance0.57390244
MonotonicityNot monotonic
2024-01-19T20:31:38.329892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2.5 541
24.4%
1 388
17.5%
1.75 345
15.6%
2 203
 
9.2%
2.25 196
 
8.8%
1.5 158
 
7.1%
2.75 119
 
5.4%
3.5 75
 
3.4%
3 74
 
3.3%
3.25 51
 
2.3%
Other values (9) 67
 
3.0%
ValueCountFrequency (%)
0.5 1
 
< 0.1%
0.75 9
 
0.4%
1 388
17.5%
1.5 158
 
7.1%
1.75 345
15.6%
2 203
 
9.2%
2.25 196
 
8.8%
2.5 541
24.4%
2.75 119
 
5.4%
3 74
 
3.3%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 3
 
0.1%
4.75 2
 
0.1%
4.5 15
 
0.7%
4.25 8
 
0.4%
4 14
 
0.6%
3.75 14
 
0.6%
3.5 75
3.4%
3.25 51
2.3%
3 74
3.3%

sqft_living
Real number (ℝ)

HIGH CORRELATION 

Distinct431
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2073.4398
Minimum420
Maximum7850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:38.511602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum420
5-th percentile920
Q11460
median1910
Q32490
95-th percentile3832
Maximum7850
Range7430
Interquartile range (IQR)1030

Descriptive statistics

Standard deviation897.05421
Coefficient of variation (CV)0.43264059
Kurtosis2.808911
Mean2073.4398
Median Absolute Deviation (MAD)510
Skewness1.2995045
Sum4596816
Variance804706.25
MonotonicityNot monotonic
2024-01-19T20:31:38.676009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2100 19
 
0.9%
1560 18
 
0.8%
1610 18
 
0.8%
1530 18
 
0.8%
1540 18
 
0.8%
1630 17
 
0.8%
1180 17
 
0.8%
1010 17
 
0.8%
1470 17
 
0.8%
1480 16
 
0.7%
Other values (421) 2042
92.1%
ValueCountFrequency (%)
420 1
 
< 0.1%
550 1
 
< 0.1%
560 1
 
< 0.1%
600 1
 
< 0.1%
620 2
 
0.1%
700 2
 
0.1%
710 2
 
0.1%
720 7
0.3%
730 3
0.1%
740 1
 
< 0.1%
ValueCountFrequency (%)
7850 1
< 0.1%
7120 1
< 0.1%
6380 1
< 0.1%
6085 1
< 0.1%
6050 1
< 0.1%
5840 1
< 0.1%
5810 1
< 0.1%
5780 1
< 0.1%
5770 2
0.1%
5710 1
< 0.1%

sqft_lot
Real number (ℝ)

HIGH CORRELATION 

Distinct1627
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13554.643
Minimum683
Maximum435600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:38.848867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum683
5-th percentile1984.8
Q15000
median7526
Q310464
95-th percentile41207.8
Maximum435600
Range434917
Interquartile range (IQR)5464

Descriptive statistics

Standard deviation29606.43
Coefficient of variation (CV)2.1842279
Kurtosis78.528563
Mean13554.643
Median Absolute Deviation (MAD)2581
Skewness7.9126428
Sum30050644
Variance8.7654072 × 108
MonotonicityNot monotonic
2024-01-19T20:31:39.005605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000 32
 
1.4%
5000 29
 
1.3%
6000 26
 
1.2%
7200 24
 
1.1%
4500 19
 
0.9%
9600 14
 
0.6%
8400 12
 
0.5%
7500 12
 
0.5%
9000 12
 
0.5%
4800 12
 
0.5%
Other values (1617) 2025
91.3%
ValueCountFrequency (%)
683 1
< 0.1%
745 1
< 0.1%
804 1
< 0.1%
809 1
< 0.1%
812 1
< 0.1%
825 1
< 0.1%
834 1
< 0.1%
844 1
< 0.1%
892 1
< 0.1%
932 1
< 0.1%
ValueCountFrequency (%)
435600 1
< 0.1%
403693 1
< 0.1%
384634 1
< 0.1%
360241 1
< 0.1%
344124 1
< 0.1%
313672 1
< 0.1%
250905 1
< 0.1%
231739 1
< 0.1%
231303 1
< 0.1%
218472 1
< 0.1%

floors
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.496166
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:39.131719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54355321
Coefficient of variation (CV)0.3632974
Kurtosis-0.36460892
Mean1.496166
Median Absolute Deviation (MAD)0.5
Skewness0.65224001
Sum3317
Variance0.2954501
MonotonicityNot monotonic
2024-01-19T20:31:39.257139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1091
49.2%
2 837
37.8%
1.5 206
 
9.3%
3 67
 
3.0%
2.5 14
 
0.6%
3.5 2
 
0.1%
ValueCountFrequency (%)
1 1091
49.2%
1.5 206
 
9.3%
2 837
37.8%
2.5 14
 
0.6%
3 67
 
3.0%
3.5 2
 
0.1%
ValueCountFrequency (%)
3.5 2
 
0.1%
3 67
 
3.0%
2.5 14
 
0.6%
2 837
37.8%
1.5 206
 
9.3%
1 1091
49.2%

waterfront
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
2204 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2217
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2204
99.4%
1 13
 
0.6%

Length

2024-01-19T20:31:39.382682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-19T20:31:39.523704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2204
99.4%
1 13
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 2204
99.4%
1 13
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2217
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2204
99.4%
1 13
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2217
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2204
99.4%
1 13
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2204
99.4%
1 13
 
0.6%

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
2006 
2
 
92
3
 
57
1
 
33
4
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2217
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2006
90.5%
2 92
 
4.1%
3 57
 
2.6%
1 33
 
1.5%
4 29
 
1.3%

Length

2024-01-19T20:31:39.634077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-19T20:31:40.012903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2006
90.5%
2 92
 
4.1%
3 57
 
2.6%
1 33
 
1.5%
4 29
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2006
90.5%
2 92
 
4.1%
3 57
 
2.6%
1 33
 
1.5%
4 29
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2217
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2006
90.5%
2 92
 
4.1%
3 57
 
2.6%
1 33
 
1.5%
4 29
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2217
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2006
90.5%
2 92
 
4.1%
3 57
 
2.6%
1 33
 
1.5%
4 29
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2006
90.5%
2 92
 
4.1%
3 57
 
2.6%
1 33
 
1.5%
4 29
 
1.3%

condition
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
3
1437 
4
564 
5
190 
2
 
23
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2217
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row4
5th row2

Common Values

ValueCountFrequency (%)
3 1437
64.8%
4 564
 
25.4%
5 190
 
8.6%
2 23
 
1.0%
1 3
 
0.1%

Length

2024-01-19T20:31:40.145206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-19T20:31:40.286366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 1437
64.8%
4 564
 
25.4%
5 190
 
8.6%
2 23
 
1.0%
1 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 1437
64.8%
4 564
 
25.4%
5 190
 
8.6%
2 23
 
1.0%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2217
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1437
64.8%
4 564
 
25.4%
5 190
 
8.6%
2 23
 
1.0%
1 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2217
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1437
64.8%
4 564
 
25.4%
5 190
 
8.6%
2 23
 
1.0%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1437
64.8%
4 564
 
25.4%
5 190
 
8.6%
2 23
 
1.0%
1 3
 
0.1%

grade
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6481732
Minimum4
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:40.430206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum12
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1509639
Coefficient of variation (CV)0.15048874
Kurtosis1.1697504
Mean7.6481732
Median Absolute Deviation (MAD)1
Skewness0.83093809
Sum16956
Variance1.324718
MonotonicityNot monotonic
2024-01-19T20:31:40.540524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 933
42.1%
8 653
29.5%
9 238
 
10.7%
6 204
 
9.2%
10 111
 
5.0%
11 47
 
2.1%
5 20
 
0.9%
12 8
 
0.4%
4 3
 
0.1%
ValueCountFrequency (%)
4 3
 
0.1%
5 20
 
0.9%
6 204
 
9.2%
7 933
42.1%
8 653
29.5%
9 238
 
10.7%
10 111
 
5.0%
11 47
 
2.1%
12 8
 
0.4%
ValueCountFrequency (%)
12 8
 
0.4%
11 47
 
2.1%
10 111
 
5.0%
9 238
 
10.7%
8 653
29.5%
7 933
42.1%
6 204
 
9.2%
5 20
 
0.9%
4 3
 
0.1%

sqft_above
Real number (ℝ)

HIGH CORRELATION 

Distinct403
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1791.4312
Minimum420
Maximum7850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:40.697257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum420
5-th percentile840
Q11200
median1560
Q32220
95-th percentile3390
Maximum7850
Range7430
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation836.47749
Coefficient of variation (CV)0.46693252
Kurtosis3.4746521
Mean1791.4312
Median Absolute Deviation (MAD)450
Skewness1.5060442
Sum3971603
Variance699694.59
MonotonicityNot monotonic
2024-01-19T20:31:40.854422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010 28
 
1.3%
1180 27
 
1.2%
1200 26
 
1.2%
1250 23
 
1.0%
1390 22
 
1.0%
1510 21
 
0.9%
1320 21
 
0.9%
1220 21
 
0.9%
1060 20
 
0.9%
1560 19
 
0.9%
Other values (393) 1989
89.7%
ValueCountFrequency (%)
420 1
 
< 0.1%
550 1
 
< 0.1%
560 2
0.1%
570 1
 
< 0.1%
580 3
0.1%
600 1
 
< 0.1%
610 1
 
< 0.1%
620 2
0.1%
660 1
 
< 0.1%
680 1
 
< 0.1%
ValueCountFrequency (%)
7850 1
< 0.1%
6380 1
< 0.1%
6085 1
< 0.1%
5770 1
< 0.1%
5710 1
< 0.1%
5670 1
< 0.1%
5480 1
< 0.1%
5450 1
< 0.1%
5250 1
< 0.1%
5020 1
< 0.1%

sqft_basement
Real number (ℝ)

ZEROS 

Distinct170
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.00857
Minimum0
Maximum2570
Zeros1346
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:41.036253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3540
95-th percentile1100
Maximum2570
Range2570
Interquartile range (IQR)540

Descriptive statistics

Standard deviation423.9148
Coefficient of variation (CV)1.5031983
Kurtosis1.8844827
Mean282.00857
Median Absolute Deviation (MAD)0
Skewness1.4960736
Sum625213
Variance179703.76
MonotonicityNot monotonic
2024-01-19T20:31:41.198952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1346
60.7%
600 25
 
1.1%
400 24
 
1.1%
700 23
 
1.0%
500 23
 
1.0%
800 22
 
1.0%
300 17
 
0.8%
750 16
 
0.7%
900 14
 
0.6%
1000 13
 
0.6%
Other values (160) 694
31.3%
ValueCountFrequency (%)
0 1346
60.7%
40 1
 
< 0.1%
50 3
 
0.1%
80 2
 
0.1%
90 3
 
0.1%
100 3
 
0.1%
110 1
 
< 0.1%
120 1
 
< 0.1%
130 4
 
0.2%
140 7
 
0.3%
ValueCountFrequency (%)
2570 1
< 0.1%
2250 1
< 0.1%
2240 1
< 0.1%
2220 1
< 0.1%
2150 2
0.1%
2060 1
< 0.1%
2020 1
< 0.1%
1900 1
< 0.1%
1870 1
< 0.1%
1852 1
< 0.1%

yr_built
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.0465
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:41.356127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2012
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.505233
Coefficient of variation (CV)0.014969324
Kurtosis-0.69137063
Mean1971.0465
Median Absolute Deviation (MAD)23
Skewness-0.44484625
Sum4369810
Variance870.55876
MonotonicityNot monotonic
2024-01-19T20:31:41.537517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 69
 
3.1%
2006 55
 
2.5%
2003 52
 
2.3%
2004 47
 
2.1%
2007 44
 
2.0%
1977 42
 
1.9%
1967 42
 
1.9%
2008 41
 
1.8%
1978 39
 
1.8%
1959 36
 
1.6%
Other values (106) 1750
78.9%
ValueCountFrequency (%)
1900 8
0.4%
1901 3
 
0.1%
1902 5
0.2%
1903 3
 
0.1%
1904 1
 
< 0.1%
1905 5
0.2%
1906 12
0.5%
1907 9
0.4%
1908 9
0.4%
1909 6
0.3%
ValueCountFrequency (%)
2015 4
 
0.2%
2014 69
3.1%
2013 25
 
1.1%
2012 18
 
0.8%
2011 14
 
0.6%
2010 10
 
0.5%
2009 22
 
1.0%
2008 41
1.8%
2007 44
2.0%
2006 55
2.5%

yr_renovated
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.330627
Minimum0
Maximum2015
Zeros2121
Zeros (%)95.7%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:41.733078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation405.89326
Coefficient of variation (CV)4.7016138
Kurtosis18.188604
Mean86.330627
Median Absolute Deviation (MAD)0
Skewness4.4911723
Sum191395
Variance164749.34
MonotonicityNot monotonic
2024-01-19T20:31:41.936512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 2121
95.7%
2005 7
 
0.3%
2000 6
 
0.3%
2010 5
 
0.2%
2013 4
 
0.2%
2003 4
 
0.2%
1989 3
 
0.1%
2014 3
 
0.1%
1999 3
 
0.1%
2015 3
 
0.1%
Other values (39) 58
 
2.6%
ValueCountFrequency (%)
0 2121
95.7%
1951 1
 
< 0.1%
1953 1
 
< 0.1%
1956 1
 
< 0.1%
1958 1
 
< 0.1%
1960 1
 
< 0.1%
1965 1
 
< 0.1%
1968 1
 
< 0.1%
1969 1
 
< 0.1%
1970 2
 
0.1%
ValueCountFrequency (%)
2015 3
0.1%
2014 3
0.1%
2013 4
0.2%
2012 1
 
< 0.1%
2010 5
0.2%
2009 2
 
0.1%
2008 1
 
< 0.1%
2007 1
 
< 0.1%
2006 1
 
< 0.1%
2005 7
0.3%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98079.107
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:42.109496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98070
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation52.95195
Coefficient of variation (CV)0.00053989022
Kurtosis-0.85409519
Mean98079.107
Median Absolute Deviation (MAD)42
Skewness0.3853781
Sum2.1744138 × 108
Variance2803.909
MonotonicityNot monotonic
2024-01-19T20:31:42.308366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98038 70
 
3.2%
98117 65
 
2.9%
98052 62
 
2.8%
98059 60
 
2.7%
98115 60
 
2.7%
98042 59
 
2.7%
98103 57
 
2.6%
98118 56
 
2.5%
98058 51
 
2.3%
98023 50
 
2.3%
Other values (60) 1627
73.4%
ValueCountFrequency (%)
98001 32
1.4%
98002 24
1.1%
98003 28
1.3%
98004 29
1.3%
98005 16
 
0.7%
98006 44
2.0%
98007 12
 
0.5%
98008 30
1.4%
98010 6
 
0.3%
98011 15
 
0.7%
ValueCountFrequency (%)
98199 31
1.4%
98198 26
1.2%
98188 15
 
0.7%
98178 25
1.1%
98177 27
1.2%
98168 39
1.8%
98166 26
1.2%
98155 44
2.0%
98148 12
 
0.5%
98146 25
1.1%

lat
Real number (ℝ)

Distinct1752
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.557274
Minimum47.1942
Maximum47.7775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:42.516675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.1942
5-th percentile47.31438
Q147.4698
median47.567
Q347.6745
95-th percentile47.74536
Maximum47.7775
Range0.5833
Interquartile range (IQR)0.2047

Descriptive statistics

Standard deviation0.13614404
Coefficient of variation (CV)0.0028627385
Kurtosis-0.72368939
Mean47.557274
Median Absolute Deviation (MAD)0.1044
Skewness-0.44161142
Sum105434.48
Variance0.018535199
MonotonicityNot monotonic
2024-01-19T20:31:42.691050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5666 5
 
0.2%
47.6681 4
 
0.2%
47.6513 4
 
0.2%
47.5265 4
 
0.2%
47.567 4
 
0.2%
47.5634 4
 
0.2%
47.6993 4
 
0.2%
47.6988 4
 
0.2%
47.3338 4
 
0.2%
47.5443 4
 
0.2%
Other values (1742) 2176
98.2%
ValueCountFrequency (%)
47.1942 1
< 0.1%
47.1947 2
0.1%
47.1948 1
< 0.1%
47.1983 1
< 0.1%
47.2016 1
< 0.1%
47.2026 1
< 0.1%
47.2048 1
< 0.1%
47.2058 1
< 0.1%
47.2068 1
< 0.1%
47.2082 1
< 0.1%
ValueCountFrequency (%)
47.7775 1
< 0.1%
47.7769 2
0.1%
47.7762 1
< 0.1%
47.7757 2
0.1%
47.7756 1
< 0.1%
47.7751 2
0.1%
47.7744 1
< 0.1%
47.7743 1
< 0.1%
47.7742 1
< 0.1%
47.774 1
< 0.1%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct511
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.21522
Minimum-122.511
Maximum-121.352
Zeros0
Zeros (%)0.0%
Negative2217
Negative (%)100.0%
Memory size17.4 KiB
2024-01-19T20:31:42.893454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122.511
5-th percentile-122.387
Q1-122.329
median-122.235
Q3-122.127
95-th percentile-121.9816
Maximum-121.352
Range1.159
Interquartile range (IQR)0.202

Descriptive statistics

Standard deviation0.14079072
Coefficient of variation (CV)-0.0011519901
Kurtosis0.83088139
Mean-122.21522
Median Absolute Deviation (MAD)0.1
Skewness0.86558437
Sum-270951.14
Variance0.019822027
MonotonicityNot monotonic
2024-01-19T20:31:43.082237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.304 18
 
0.8%
-122.35 15
 
0.7%
-122.285 14
 
0.6%
-122.298 13
 
0.6%
-122.391 13
 
0.6%
-122.352 13
 
0.6%
-122.291 13
 
0.6%
-122.376 12
 
0.5%
-122.287 12
 
0.5%
-122.289 12
 
0.5%
Other values (501) 2082
93.9%
ValueCountFrequency (%)
-122.511 2
0.1%
-122.509 1
< 0.1%
-122.497 1
< 0.1%
-122.484 1
< 0.1%
-122.482 1
< 0.1%
-122.474 1
< 0.1%
-122.463 1
< 0.1%
-122.462 1
< 0.1%
-122.448 1
< 0.1%
-122.44 1
< 0.1%
ValueCountFrequency (%)
-121.352 1
< 0.1%
-121.707 1
< 0.1%
-121.709 1
< 0.1%
-121.714 1
< 0.1%
-121.718 1
< 0.1%
-121.735 1
< 0.1%
-121.738 1
< 0.1%
-121.745 1
< 0.1%
-121.746 1
< 0.1%
-121.747 1
< 0.1%

sqft_living15
Real number (ℝ)

HIGH CORRELATION 

Distinct363
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1985.8751
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:43.259304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1830
Q32370
95-th percentile3282
Maximum6210
Range5811
Interquartile range (IQR)880

Descriptive statistics

Standard deviation686.14912
Coefficient of variation (CV)0.34551475
Kurtosis2.116041
Mean1985.8751
Median Absolute Deviation (MAD)410
Skewness1.1845908
Sum4402685
Variance470800.61
MonotonicityNot monotonic
2024-01-19T20:31:43.433201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1420 23
 
1.0%
1540 23
 
1.0%
1800 22
 
1.0%
1500 22
 
1.0%
1530 22
 
1.0%
1770 21
 
0.9%
1300 21
 
0.9%
1640 20
 
0.9%
1390 20
 
0.9%
1690 20
 
0.9%
Other values (353) 2003
90.3%
ValueCountFrequency (%)
399 1
 
< 0.1%
620 1
 
< 0.1%
750 1
 
< 0.1%
780 1
 
< 0.1%
830 2
0.1%
840 2
0.1%
860 3
0.1%
870 1
 
< 0.1%
880 1
 
< 0.1%
900 4
0.2%
ValueCountFrequency (%)
6210 1
< 0.1%
5600 1
< 0.1%
5330 1
< 0.1%
4850 1
< 0.1%
4830 1
< 0.1%
4760 1
< 0.1%
4620 2
0.1%
4560 1
< 0.1%
4480 1
< 0.1%
4470 1
< 0.1%

sqft_lot15
Real number (ℝ)

HIGH CORRELATION 

Distinct1582
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12147.815
Minimum755
Maximum292645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-01-19T20:31:43.590002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum755
5-th percentile2268.4
Q15078
median7551
Q310000
95-th percentile36430.8
Maximum292645
Range291890
Interquartile range (IQR)4922

Descriptive statistics

Standard deviation22904.987
Coefficient of variation (CV)1.8855232
Kurtosis56.973284
Mean12147.815
Median Absolute Deviation (MAD)2463
Skewness6.9473654
Sum26931706
Variance5.2463841 × 108
MonotonicityNot monotonic
2024-01-19T20:31:43.747245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 46
 
2.1%
4000 43
 
1.9%
6000 29
 
1.3%
7200 23
 
1.0%
7800 14
 
0.6%
7350 13
 
0.6%
9600 11
 
0.5%
4080 11
 
0.5%
7500 11
 
0.5%
5500 11
 
0.5%
Other values (1572) 2005
90.4%
ValueCountFrequency (%)
755 1
< 0.1%
824 1
< 0.1%
886 1
< 0.1%
942 2
0.1%
955 2
0.1%
1003 1
< 0.1%
1007 1
< 0.1%
1026 1
< 0.1%
1062 1
< 0.1%
1079 1
< 0.1%
ValueCountFrequency (%)
292645 1
 
< 0.1%
275299 1
 
< 0.1%
220849 1
 
< 0.1%
217800 3
0.1%
212137 1
 
< 0.1%
211404 1
 
< 0.1%
209959 1
 
< 0.1%
207781 1
 
< 0.1%
202554 1
 
< 0.1%
199504 1
 
< 0.1%

Interactions

2024-01-19T20:31:33.575590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:50.868420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:53.438264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:56.045863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:58.717512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:01.137409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:03.689474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:06.133768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:08.603520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:11.329333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:14.562151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:16.917633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:19.663717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:22.606326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:25.356261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:28.028453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:30.721833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:33.716779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:51.022485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:53.586657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-01-19T20:31:35.423195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:52.864152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:55.429515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:58.129830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:00.572546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:03.097889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:05.581639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:08.010108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:10.698531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:13.867592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:16.376953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:19.074959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:21.952902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:24.718861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:27.429396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:30.084804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:33.025661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:35.568902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:53.013088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:55.579617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:58.278229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:00.720351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:03.249486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:05.726524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:08.153290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:10.850720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:14.070976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:16.509514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:19.240686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:22.130374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:24.868905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:27.581455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:30.245912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:33.160794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:35.730758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:53.156749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:55.726597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:58.424589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:00.858666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:03.396100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:05.861919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:08.321694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:11.001596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:14.235945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:16.650630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:19.385335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:22.295539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:25.028075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:27.735518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:30.402729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:33.306887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:35.879570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:53.295525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:55.874770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:30:58.566974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:00.996971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:03.540073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:06.001192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:08.464765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:11.157838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:14.392880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:16.778077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:19.522598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:22.443767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:25.184580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:27.882214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:30.555929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-19T20:31:33.443043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-01-19T20:31:43.903951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradeidlatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyr_builtyr_renovatedzipcode
bathrooms1.0000.5220.1240.5610.6720.0430.0120.2700.4880.6910.1580.7450.5680.0410.0440.1430.0520.6060.022-0.213
bedrooms0.5221.0000.0660.2540.4050.038-0.0000.2160.3480.5490.1790.6430.4440.2140.2060.0300.0760.218-0.012-0.186
condition0.1240.0661.000-0.307-0.201-0.0180.010-0.1090.016-0.1900.155-0.100-0.1050.1100.1110.0120.000-0.387-0.072-0.002
floors0.5610.254-0.3071.0000.5130.0490.0250.1660.3140.610-0.2840.4190.323-0.237-0.2180.0310.0000.5630.015-0.082
grade0.6720.405-0.2010.5131.0000.0460.0940.2230.6260.7160.0740.7260.6640.1140.1350.1460.1200.5300.007-0.191
id0.0430.038-0.0180.0490.0461.0000.0000.0590.0230.041-0.0290.0230.034-0.120-0.1010.0140.0530.0340.014-0.028
lat0.012-0.0000.0100.0250.0940.0001.000-0.1710.494-0.0130.1360.0480.040-0.115-0.1160.0700.000-0.1430.0220.257
long0.2700.216-0.1090.1660.2230.059-0.1711.0000.0300.373-0.2230.2550.3730.3690.3710.0990.1610.421-0.087-0.580
price0.4880.3480.0160.3140.6260.0230.4940.0301.0000.5240.2540.6390.5630.0400.0430.2270.3420.1080.1140.015
sqft_above0.6910.549-0.1900.6100.7160.041-0.0130.3730.5241.000-0.2000.8420.6930.2490.2450.0880.1300.4900.040-0.276
sqft_basement0.1580.1790.155-0.2840.074-0.0290.136-0.2230.254-0.2001.0000.2970.098-0.010-0.0190.1860.079-0.2000.0580.145
sqft_living0.7450.643-0.1000.4190.7260.0230.0480.2550.6390.8420.2971.0000.7340.2590.2490.1640.0900.3660.058-0.193
sqft_living150.5680.444-0.1050.3230.6640.0340.0400.3730.5630.6930.0980.7341.0000.3210.3400.1210.0910.3490.004-0.297
sqft_lot0.0410.2140.110-0.2370.114-0.120-0.1150.3690.0400.249-0.0100.2590.3211.0000.9210.0550.116-0.0470.024-0.301
sqft_lot150.0440.2060.111-0.2180.135-0.101-0.1160.3710.0430.245-0.0190.2490.3400.9211.0000.0890.176-0.0150.015-0.304
view0.1430.0300.0120.0310.1460.0140.0700.0990.2270.0880.1860.1640.1210.0550.0891.0000.614-0.0670.1590.097
waterfront0.0520.0760.0000.0000.1200.0530.0000.1610.3420.1300.0790.0900.0910.1160.1760.6141.000-0.0420.1560.013
yr_built0.6060.218-0.3870.5630.5300.034-0.1430.4210.1080.490-0.2000.3660.349-0.047-0.015-0.067-0.0421.000-0.220-0.337
yr_renovated0.022-0.012-0.0720.0150.0070.0140.022-0.0870.1140.0400.0580.0580.0040.0240.0150.1590.156-0.2201.0000.092
zipcode-0.213-0.186-0.002-0.082-0.191-0.0280.257-0.5800.015-0.2760.145-0.193-0.297-0.301-0.3040.0970.013-0.3370.0921.000

Missing values

2024-01-19T20:31:36.111962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-19T20:31:36.526524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0379350016020150312T000000323000.032.50189065602.0003718900200309803847.3684-122.03123907570
1117500057020150312T000000530000.052.00181048501.5003718100190009810747.6700-122.39413604850
21600039720141205T000000189000.021.00120098501.0004712000192109800247.3089-122.21010605095
346100039020140624T000000687500.041.75233050001.500471510820192909811747.6823-122.36814605000
4789550007020150213T000000240000.041.00122080751.00027890330196909800147.3341-122.28212907800
5362603927120150205T000000585000.021.75198085501.00037990990198109811747.6989-122.36914806738
6118900118020140603T000000425000.032.25166060001.000371110550197909812247.6113-122.29714404080
7721472007520141212T000000699950.032.2521901075932.0004821900198309807747.7731-122.080257047777
8132831037020150402T000000375000.032.502340100051.000481460880197809805847.4431-122.13322508162
9406000024020140623T000000205425.021.0088067801.000468800194509817847.5009-122.24811906780
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
2207293730004020141215T000000942990.042.50357062182.0003935700201409805247.7046-122.12332305972
2208743050011020141209T0000001380000.053.505150122302.00231037001450200709800847.6249-122.090294013462
2209330430030020150507T000000579950.042.75246086432.0003924600201109805947.4828-122.13331108626
2210645355009020150505T000000861111.042.50365070902.00031036500200809807447.6060-122.05238607272
2211957806023020140618T000000535000.042.50261045952.0003826100200809802847.7728-122.23524404588
2212666908012020141215T000000405000.042.50198050202.0003719800200709805647.5147-122.19019805064
2213285500011020140808T000000388000.032.50219862222.0023821980201009819847.3906-122.30421987621
2214334570020720150502T000000608500.043.50285055772.000381950900201409805647.5252-122.19228505708
2215605611106720140707T000000230000.031.75114012012.0003811400201409810847.5637-122.29512101552
2216276760068820141113T000000414500.021.50121012782.000381020190200709811747.6756-122.37512101118